The AI Research Division of Agile Robots is looking for a Software Engineer (m/f/d) Robotics Research Tooling and Reliability who would take responsibility for the usability and reliability of the toolchain that our teams depend on daily, from dataset viewers, training dashboards, benchmark interfaces until the end-to-end test infrastructure that keeps them stable. The job is to make the tooling ecosystem trustworthy under pressure.

• Tool Surfaces: Build and maintain UI, API, and CLI interfaces (dataset viewers, training dashboards, benchmark viewers) that researchers use for daily inspection and monitoring work.
• End-to-End Testing: Design and maintain E2E test suites that simulate real user workflows from data ingestion through training to inference, catching regressions before they reach production.
• Stress Testing: Run stress tests under realistic lab constraints (messy data, constrained compute, edge-case queries) to ensure tooling degrades gracefully rather than failing silently.
• Golden Path Maintenance: Maintain a reference test suite and nightly regression runs that define the canonical data-to-training-to-inference workflow and prevent drift.
• Demo Reliability: Own reliability checks and regression prevention for critical demos and evaluations, ensuring the toolchain survives the pressure of time-sensitive events.

• Backend Engineering: Strong experience building production-quality backend services for internal or technical users, ideally in Python and/or TypeScript, with attention to reliability, debugging, and maintainability under real load.
• Testing Discipline: Hands-on experience designing integration and end-to-end test suites, CI-based regression workflows, and failure analysis across multi-step user journeys.
• Research Tooling: Experience building internal tools, workflow surfaces, or platform components used by ML engineers, researchers, or other data-heavy technical teams.
• User Empathy: Proven ability to improve technical tools based on how users actually work rather than only on idealized requirements or abstract feature requests.
• Engineering Practice: Solid grounding in version control, code review, observability, and clear documentation for maintainable engineering systems.

• Frontend Development: Experience with a modern frontend framework such as React for building data-intensive interfaces, dashboards, inspection tools, or similar internal technical surfaces.
• ML Workflow Context: Familiarity with experiment tracking, benchmark systems, dataset inspection workflows, or training and evaluation pipelines in ML-heavy environments.
• Documentation Habits: Practice of maintaining clear, current documentation as part of the engineering surface rather than treating it as an afterthought.

• Dynamic high-tech company combined with financial soundness and world class investors.
• Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment.
• Lots of development opportunities in the context of our continued growth.
• Challenging tasks and impactful projects alongside experts that enable professional and personal growth.
• Corporate Benefits Program that covers health, mobility and learning with 100 € net per month.
• Modern office facilites with a rooftop terrace overlooking Munich, free drinks & fruits, and regular company events contribute to a good working environment.